Digital documents often need to be resized to fit the device whereon the document image is intended to be displayed to the user, such as a mobile phone, camera, PDA, monitors, printers, and the like. Document images can be resized by a cropping or scaling technique which treat the document as a single, opaque object. Cropping works reasonably well for shrinking images if there is only one region of interest in the document intended to be resized. Naïve cropping can be problematic because important contextual information may be removed from the document. Scaling works reasonably well for shrinking images containing low frequency information. However, scaling can be of limited value because scaling tends to be uniformly applied to the overall image resulting in information loss. With proper region identification, cropping may be preferred over naïve scaling.
Content-aware resizing techniques have been developed owing to the inherent limitations of classical scaling and cropping-based resizing methods. Given that many widely employed document formats, such as PDF and WORD, enable the composition of documents incorporating various different objects such as images, text, graphics, and the like, treating a document page as a flattened raster image is inherently limiting from a resizing standpoint. Because document parsers are often an essential software component in features such as object-optimized color rendering, a framework for resizing that employs customized algorithmic approaches based on the various object types contained within a document is desirable.
Accordingly, what is needed in this art are document resizing methods that utilize segmentation information obtained from the document itself to selectively resize objects within the document using the most appropriate technique for each identified object.